A calibration method of a spectroscopy device comprising a plurality of sensors and of transfer of spectral information obtained from at least two calibrated spectroscopy devices

ABSTRACT

The present invention is enclosed in the area of calibration of spectral information/spectroscopy devices, such as the calibration of spectral information which consist of high-resolution electromagnetic spectra, as Laser-induced Breakdown Spectroscopy (LIBS). It is an object of the present invention a calibration method of a spectroscopy device comprising a plurality of sensors and a method for transferring spectral information obtained from a first and a second spectroscopy devices. The method of the present invention provides the access to accurately defined spectral lines from the electromagnetic spectrum, as well as to obtain electromagnetic spectra in two different sites, with two different spectroscopy devices and physical samples still provide for the reliable comparison between the electromagnetic spectra obtained in each of such spectroscopy devices.

FIELD OF THE INVENTION

The present invention is enclosed in the area of calibration of spectralinformation, such as the calibration of spectralinformation/spectroscopy devices—one or more electromagneticspectra—which consist of high-resolution electromagnetic spectra, aselectromagnetic spectra obtained by means of Laser-induced BreakdownSpectroscopy (LIBS). The present invention also provides for thetransfer of spectral information obtained in two calibrated spectroscopydevices, spectral information obtained in a calibrated spectroscopydevice being comparable with spectral information obtained in the othercalibrated spectroscopy device.

PRIOR ART

Several high-resolution spectroscopy techniques are known in the art,such as Plasma emission spectroscopy, in particular Laser InducedBreakdown Spectroscopy (LIBS), Mass Spectroscopy (MS), X-RayFluorescence (XRF) or Nuclear magnetic resonance spectroscopy (NMR).High-resolution spectroscopy techniques provide high-resolutionelectromagnetic spectra with at least a picometer resolution.

The identification of chemical elements, molecules and their structurecould be performed by direct spectral matching as obtained from suchtechniques against certified databases (Kramida et al., 2018), ifinfinite optical resolution and no uncertainties exist due to quantum,Doppler and collisional broadening and optical resolution. In real worldhowever, spectral information obtained from a physical sample is theresult of complex super-position and convolution of the previousphysical phenomena, generating multi-scaled interference of spectralinformation due to optical resolution limits and spectral linesbroadening effects.

These broadening effects and artefacts make it nearly impossible tovalidate the assumption that all spectral lines of a pure elements areexclusive information that allows a direct identification. In thiscontext, line matching algorithms at optical resolutions are likely tofail element identification. Such is a very significant limitation forsuch high-resolution spectroscopy techniques, because many elements havesignificant number of overlapping band regions, as they have an elevatednumber of lines that may interfere with other elements.

Referring specifically to LIBS, as an example, state-of-the artplasma-emission spectroscopy systems work with pixel-based methods.These have limited success, because convoluted spectral bands do notallow a deterministic identification of constituents present in aphysical sample by their spectral lines. During this process,unnecessary interference and uncertainty is introduced, constrainingpixel-based methods to probabilistic identification, classification andquantification. Furthermore, today's methods cannot resolve spectralline doublet or the existence of isotopes, as these lines are generallyconvoluted below the optical resolution. The same is true for extractingplasma breakdown information, because peak broadening and spectrometersintegration time force the information about electronic transitions tobe both super-imposed and convoluted in wavelengths and time dimensions.

The same effects can be observed in other high-resolution spectroscopytechniques, except in that each sensor does not consist of acharge-coupled device (CCD), therefore forming a pixel, insteadcontaining other forms of binning of information in the respectivedetectors.

Still in the example of LIBS, the full potential of such technique isprovided by the interpretation of the dynamical information structure ofemission lines acquired during the molecular breakdown ionizationprocess, whereby each different constituent has a spectral fingerprint.This dynamical ‘fingerprint’ contains all the information about chemicalelements and/or their isotopes, molecules and/or their conformations,states and structure present in a physical sample. The plasma emissionis typically used in the analysis of complex samples/mixtures ofsubstances, either occurring in nature or man-made.

The mentioned drawbacks of current techniques mean that the capabilityof state-of-the-art methods to identify, quantify, and predict thecomposition of a physical sample is still highly dependent on previousknowledge by a human expert (Hahn and Omenetto, 2010), and thedevelopment of models for identification and quantification is dependenton providing a correct context to spectral line identification (Cousinet al., 2011).

The present solution innovatively overcomes such issues.

SUMMARY OF THE INVENTION

It is an object of the present invention a calibration method of aspectroscopy device comprising a plurality of sensors, the calibrationmethod comprising the steps of:

-   -   i) obtaining an electromagnetic spectrum of a physical sample,        the electromagnetic spectrum being obtained by means of the        plurality of sensors of the spectroscopy device;    -   ii) obtaining, by determining peak groups of the electromagnetic        spectrum within a wavelength interval from a plurality of        predefined wavelength intervals, and matching each peak group        with at least one theoretical spectral line within such        interval, at least one spectral line group from the        electromagnetic spectrum, a spectral line group containing at        least one spectral line,    -   iii) optimising a deconvolution of each obtained spectral line        group against at least one theoretical electromagnetic spectrum,        and thereby extracting at least one spectral line from each        spectral line group, in particular obtaining a wavelength        associated to each extracted spectral line, preferably the        optimisation being performed until convergence of each spectral        line group with an at least one spectral line of a theoretical        spectrum, with a predefined minimum error,    -   iv) assigning each obtained wavelength to one or more sensors of        the plurality of sensors of the spectroscopy device, and thereby        corresponding each wavelength to a wavelength position in the        whole sensor length, the sensor length being defined by the        plurality of sensors, and    -   v) from the correspondence of each wavelength to a wavelength        position in the sensor length, determining a calibration        function of the spectroscopy device.

Therefore, in comparison to the state-of-the-art that is based onsensor-based technology (such as pixel-based, in the case of LIBS): themethod of the present invention provides the access to accuratelydefined spectral lines, allows the deterministic assignment of observedspectral lines to their expected theoretical wavelengths and transitionprobabilities (Kramida et al, 2018). It allows to accurately obtain thecalibration function of a spectroscopy device, therefore establishingthe basis for transfer of spectral information obtained from differentspectroscopy devices. In prior art systems, for the spectral informationbetween two physical samples to be comparable, it requires that the samespectroscopy device is used. Moreover, and as will be describedsubsequently, and by accurately obtaining the calibration function of aspectroscopy device—as well as accurately defined spectral lines, themethod of the present invention also establishes the basis foraccurately defining consistently observed spectral lines toself-assemble resolution invariant spectral lines databases—in the caseof LIBS, additionally using dynamic breakdown spectral information—andallowing the automated construction of distributed spectral linesdatabases, where the data is obtained from independent spectroscopydevices and providing a network of apparatuses containing databases withspectral information of the said spectroscopy devices, contributing fora common big data emission spectral lines database—in the case of LIBS,with plasma-breakdown information. Consistent observable lines means amatch against the theoretical SAHA/LTE spectra both in terms of grouprank position and their intensity, as will be described subsequently,the match being classified as perfect when within a minimum predefinederror.

The method of the present invention changes the paradigm associated withprior art methods, by using only sub-optical spectral information, i.e.,extracting spectral lines below the optical resolution of thespectroscopy device. Such is possible, because sensor density is higherthan optical resolution, and spectral lines incident on each sensor arebroadened through consecutive sensors (in the case of LIBS, CCDs).Therefore, determining a spectral line position—from such spectralinformation, avoids the uncertainty associated with sensor-based methods(pixel-based method, in the case of LIBS). Moreover, ultra-lowwavelength error in spectral lines is relevant for extraction ofconstituent information for identification, classification,quantification and determining the chemical structure from theelectromagnetic spectra. As regards a LIBS based method, extremely lowerror in the determination of spectral lines, turns the identificationof elements or small molecules ion emission, a deterministic process,opposing to a probabilistic process in previous sensor-based methods,that is, identification models had to be based on uncertainty o spectralline sensor position.

Sub-optical spectral data is a consequence of the method disclosedherein to extract spectral lines with improved accuracy, enabling theidentification of constituents in complex physical samples. Sub-opticalresolution is the determination of spectral lines below the opticalresolution of the spectrometer/spectroscopy device usingsuper-resolution achieved by sub-optical continuous sensor calibrationand deconvolution techniques to remove the convolution artefactsintroduced by the components of the spectroscopy device—such as opticalcomponents, in the case of LIBS. Sub-optical spectral data is used asfeature variables to identify and/or quantify one or more constituentsin a physical sample.

As is clear from the above description, the method of the presentinvention may be implemented by an individual computational apparatuswhich obtains the electromagnetic spectrum and the information on thespectroscopy device, namely the sensor length, the apparatus notcomprising the specific spectroscopy device itself. The computationalapparatus may also comprise the spectroscopy device, although notrequired.

The referred theoretical electromagnetic spectrum may consist of aSaha/LTE emission spectrum, such as Saha/LTE emission spectra ofparticular elements, thereby providing consistency between the obtainedelectromagnetic spectrum and the theoretical electromagnetic spectrum.

A physical sample contains constituents, each constituent consisting ofone or combinations of chemical elements and/or their isotopes,molecules and/or their conformations or states.

Provided the sub-optical calibration of the method of the presentinvention, it enables automatic self-assembly of spectral line databasesby: i) performing supervised sub-optical deconvolution usingtheoretically consistent spectral lines; ii) digitally transferringsample spectral lines information across a network of computationalapparatuses which may contain spectroscopy devices, maintaining theconsistency of spectral lines wavelengths independently of spectralresolution and corresponding intensities; and iii) generating thedistributed spectral lines database that can be used as the source ofknowledge database across a network of spectrometer devices. By means ofspecific embodiments of the method of the present invention, it ispossible to create distributed spectral information databases that canbe further used by a multitude of different devices.

In an embodiment which will be subsequently described in detail, themethod self-assembles spectral information databases from existing ornew added data and self-diagnoses about the consistency of the spectrallines of an obtained electromagnetic spectrum with spectral informationin such databases. It further provides to supervise which spectral linesshould be used by using the theoretically consistent emission spectrallines. The capacity of autonomous continuous update and interactionwithout human interpretation, is more and more necessary forapplications in areas of complex variability, such as, geology, medicineand biotechnology; where big-data databases of high resolutionspectroscopy techniques do not exist and validation by human labour isnot feasible. The method of the present invention is thus a horizontaltechnology applicable to fields where minimally destructive andminimally invasive applications are mostly needed, such as: health-care,animal care, biotechnology, pharmaceuticals, food and agriculture, rawmaterials and minerals, micro and nanotechnology, molecular biology,inland security and military, chemical and nano-engineered materials. Itdoes not require preparation of physical samples in a laboratory. Thespectral information of the present method is preferably obtained from atechnology which enables plasma inducement, namely LIBS.

Moreover, it is also an object of the present invention a method forassembling at least one electromagnetic spectrum database whichcomprises the steps of:

-   -   calibrating a spectroscopy device through the method of any of        the preceding claims,    -   obtaining an electromagnetic spectrum of a physical sample by        means of the plurality of sensors of the calibrated spectroscopy        device,    -   obtaining at least one spectral line from the electromagnetic        spectrum of the calibrated spectroscopy device and determining        one or more constituents present in such physical sample, and    -   storing such at least one spectral line obtained from the        electromagnetic spectrum X and corresponding determined one or        more constituents Y in said at least one electromagnetic        spectrum database, thereby assembling an entry corresponding to        a sample.

Thus, the method of the present invention makes use of a high-resolutionsub-optical electro-magnetic spectrum, obtained by the said latentthermodynamic equilibrium or/and dynamical emission spectra, to extractthe correspondent spectral lines and determine their wavelengths bymatching the line position in the continuous sensor length of the sensorwavelength calibration function. From the extracted spectral lines,consistent spectral lines are determined with a database, and may beclassified into exclusive (lines that exist for a particularconstituent), interference (lines that interfere with other lines fromother constituents) and unique (spectral lines that are exclusive of aplasma-breakdown process and particular of a particular molecularstructure). These consistent spectral lines may constitute the saidassembled database, providing knowledge about constituents at aparticular optical resolution.

It is yet an object of the present invention a method for transferringspectral information obtained from a first spectroscopy device i and atleast a second spectroscopy device j, in a supervised fashion and in anunsupervised fashion. The particulars of such inventive aspects of thepresent invention are detailed below.

Furthermore, it is also an object of the present invention acomputational apparatus for the calibration of a spectroscopy devicecomprising a plurality of sensors, wherein it is configured to implementthe calibration method of the present invention or the assembly methodof the present invention or the supervised and unsupervised spectralinformation transfer methods of the present invention, optionallyfurther comprising a spectroscopy device which:

-   -   is able to induce a plasma state in a physical sample, said        spectral information being obtained from said spectroscopy        device, the spectroscopy device preferably consisting of a        plasma inducing spectroscopy technique, more preferably a LIBS        device,    -   consists of an MS device, the sensors thereby consisting of at        least one MS detector,    -   consists of an XRF device, the sensors thereby consisting of at        least one XRF detector, or    -   consists of an NMR device, the sensors thereby consisting of at        least one NMR detector.

Additionally, it is also a part of the present invention a network ofcomputational apparatuses, each computational apparatus comprising adatabase and being configured to implement the database assembly methodof the present invention, thereby assembling such database, eachcomputational apparatus being further configured to implement thesupervised and unsupervised spectral information transfer methods of thepresent invention wherein, for each computational apparatus, any sampleentry obtained by means of a first spectroscopy device is comparablewith any sample entry obtained by means of the second spectroscopydevice. As referred, it provides the automated construction ofdistributed spectral lines databases, where the data is obtained fromindependent spectroscopy devices and providing a network of apparatusescontaining databases with spectral information of the said spectroscopydevices, contributing for a common big data emission spectral linesdatabase—in the case of LIBS, with plasma-breakdown information.

A non-transitory storage media including program instructions executableto carry out the calibration method, the assembly method and/or thespectral information transfer methods of the present invention, in anyof their described embodiments, is also part of the present invention.

DESCRIPTION OF FIGURES

FIG. 1 contains a representation of an embodiment of the assembly methodof the present invention, which enables to build the one or more(distributed) self-assembled spectral lines databases. In the firstcolumn, it presents a possible set of steps of the calibration method ofthe present invention, for a case of a spectroscopy device whichcontains two groups of sensors, which thereby require merge. Themiddle/second column presents a possible set of steps for the method ofspectral information transfer between a first calibrated spectroscopydevice (CCD1) and a second calibrated spectroscopy device (CCD2). Theright/third column presents a network of databases, each containingspectral information obtained from a spectroscopy device, the network ofdatabases defining a global knowledge database (knowledgebase). Theremaining figures are for an illustrative case of electromagneticspectrum/spectra obtained by means of LIBS.

FIG. 2 (left) presents a typical dynamical LIBS signal (12) thatencompasses a time series from laser ablation to ion emission at thelatent thermodynamic equilibrium (LTE) (13), and corresponding mainsteps (right) for assembling sub-optical dynamic emission spectrallines. Such is performed by sub-optical spectral lines extraction fromsample emission spectra (14) that are consistent with theoretical ionemission lines (15). The extracted emission lines (λ) along time (t),comprising the dynamic emission databases D(S,λ,t) (16), from which dataat the LTE can also be extracted (17).

FIG. 3 is a schematic representation of the continuous sub-optical CCDcalibration step.

FIG. 4 presents an embodiment with the specific steps of the sub-opticalcontinuous wavelength calibration (spectral line group extraction,deconvolution into spectral lines) for achieving sub-opticalwavelength-CCD length correspondence.

FIG. 5 illustrates an implementation of the specific embodiment of FIG.4, with peak binning, relative intensity correction, and rank matchingsteps that are involved in spectral lines matching and constitute theinitial steps of the sub-optical continuous wavelength calibrationprocess.

FIG. 6 presents how the binning, intensity correction, rank and matchingmethods steps deal with non-observed theoretical emission spectral lineswith the particular example of mercury (Hg) emission spectra (35), wherefor a particular CCD (37) belonging to a multiple CCD system.

FIG. 7 is a schematic representation of the supervised deconvolutionmethodology using non-negative solution space, with extreme raysboundaries, that spawn and constrain the solution space to combinationsof the deconvolution of the observable spectra (O), SAHA/LTE expectedspectrum convolution (P) and corresponding spectral lines contributions(C) for the observable spectra. The illustrated procedure uses thematched groups (36) and SAHA/LTE (37) to supervise the deconvolution ofthe observed spectra bands (38) by optimizing at the same time theconvolution of the expected SAHA/LTE spectra and observed spectra byj=argmin(|O−CS^(t)|) (38). The end result is the matching of theobserved with the theoretically expected lines intensities andbroadening effects (39), from where is possible to assign spectral linesto the CCD length with sub-optical accuracy.

FIG. 8 consists of an illustrative representation presenting the mainsteps for dynamic/LTE database construction.

FIG. 9 is a schematic and illustrative representation of supervised (55)and unsupervised (59) sub-optical spectral information transfer.Supervised spectral information transfer, uses the constituentsquantification (Y) obtained by standard laboratory methodologies andspectral data (X), obtained by different apparatuses, forming individualpairwise databases of n independent apparatuses: [Y₁X₁] [Y₂X₂] [Y₃X₃] .. . [Y_(n)X_(n)] (56). In the supervised step, the Y feature-space isknown, and therefore, direct spectral-features supervision is possiblebetween each feature space of X₁, X₂, X₃ . . . X_(n) (57). Theunsupervised step (59) makes use of only spectral information propertiesin the feature-space of X₁, X₂, X₃ . . . X_(n), that must retain thesame co-variance between samples within feature space regions (S61). Inthe supervised step, each device maximizes the spectral featuresco-variance against the constituent composition, so that, each devicehas its own corresponding feature-space T₁, T₂, T₃ . . . T_(n). Thisinformation can be exchanged between devices in two distinct ways:

-   -   Synchronized samples (57)—samples that have spectra and        compositional similarities, albeit not equal, covering a        particular region of the feature-space. Such allows to create a        global feature space (T) that can be used to diagnose        information and accelerate the database development of new        apparatuses with manufactured with null databases;    -   Chain of samples—samples from different apparatuses are        neighbours in the feature space, and therefore, a network chain        of devices can transfer information between consecutive        feature-space regions, by supervised (58) or unsupervised steps        (S62). Chain information transfer is performed between X₁→X₂→X₃→        . . . →X_(n), where X_(i+2) is never synchronized with X_(i).        Allowing to transfer information of unknown regions of the        feature-space of a particular apparatus, covering the        feature-space of all apparatuses (T).

FIG. 10. presents auxiliary information about the main steps of bothsupervised and unsupervised spectral information transfer:

DETAILED DESCRIPTION

The more general and advantageous configurations of the presentinvention are described in the Summary of the invention. Suchconfigurations are detailed below in accordance with other advantageousand/or preferred embodiments of implementation of the present invention.

In a preferred embodiment of the calibration method of the presentinvention, obtaining at least one spectral line under step ii)comprises:

-   -   binning each peak group of the electromagnetic spectrum, a peak        group containing at least one peak, by determining peak groups        of the electromagnetic spectrum within a wavelength interval        from a plurality of predefined wavelength intervals and by        comparing peak groups within such predefined wavelength        intervals with spectral lines of a theoretical spectrum, wherein        the number of peak groups of the electromagnetic spectrum and of        the theoretical spectrum are the same,    -   correcting the relative intensity of each binned peak group and        defining a rank of each peak group according to a corresponding        corrected intensity, said correction being performed by        comparison with the intensities of corresponding theoretical        spectral lines, and    -   matching the rank of each peak group, by iteratively assigning a        wavelength position of a peak group and corresponding a        corrected intensity with at least one theoretical spectral line        within such interval, and thereby obtaining one or more spectral        line groups.

Such specific method provides a more reliable deconvolution in stepiii), as peaks in the obtained electromagnetic spectrum are organised inpeak groups, their intensity is corrected and the groups are ranked,thereby delivering one or more spectral line groups which allow tobetter identify spectral lines in the deconvolution of step iii).

In particular, the peak binning of step a) may further compriseperforming wavelength distance clustering between the obtained peakgroups and corresponding theoretical spectral lines, thereby determiningpeak groups of the electromagnetic spectrum within a wavelengthinterval.

Moreover, the comparison with the intensities of correspondingtheoretical spectral lines of step b) may further comprise:

-   -   determine the number of spectral lines of the theoretical        electromagnetic spectrum inside a peak group of the obtained        electromagnetic spectrum, and    -   if such peak group is centred in one or more sensors of the        spectroscopy device, divide the energy of such peak group        between the number of spectral lines of the theoretical        electromagnetic spectrum, or, alternatively,    -   if the peak group is convoluted along more than one sensor,        determine the total energy of the peak group in such sensors and        divide such total energy by the number of convoluted theoretical        spectral lines,        thereby correcting the relative intensity of obtained peak        groups based on the corresponding theoretical spectral lines.

In another particular embodiment for obtaining at least one spectralline under step ii), the rank matching of step c) specificallycomprises:

-   -   performing SS=n−k rank search sequences (SS), n consisting of        the number of peak groups and k being an integer between 3 and        n, with matching between peak groups of the obtained        electromagnetic spectrum and spectral lines of the theoretical        electromagnetic spectrum,    -   sorting peak groups by their intensities until a global rank        match is established, the global rank match being established        when a full length is reached, thereby obtaining one or more        spectral line groups, and    -   where a peak group does not match in wavelength position and        intensity with a spectral line of the theoretical spectrum,        discarding such peak group, thereby providing a complete match        between peak groups of the obtained electromagnetic spectrum and        spectral lines of the theoretical electromagnetic spectrum.        Discarding a certain peak group may be described as dropping        such peak group, to provide a more complete match.

In another inventive aspect of the calibration method of the presentinvention, the deconvolution of step iii) may further compriseoptimising the wavelength position and intensities of spectral lineswithin a spectral line group between each spectral line group and atheoretical electromagnetic spectrum, specifically by means of theoptimisation of similarity and wavelength position invariance betweeneach spectral line group and a theoretical electromagnetic spectrum and,preferably, such optimisation comprising the estimation of thewavelength position of theoretical spectral lines by deconvolution ofthe obtained electromagnetic spectrum (O) and convolution of thereferred theoretical spectral lines (P) within a spectral line group, bynon-negative optimization of:

j=argmin(|O _(dec) −CP _(conv) ^(T)|),

where C is a non-negative matrix which defines a convolution andsuperposition of spectral lines, P^(T) consisting of the transposed ofP. The deconvolution of the obtained spectrum (O)—thereby obtaining adeconvoluted O (O_(dec))—and the convolution of the referred theoreticalspectral lines (P)—thereby obtaining a convoluted P (P_(conv))—are sooptimised aiming a match between O and P, thereby extracting at leastone spectral line. Such particular method provides a reliable way ofdeconvoluting spectral lines from a spectral line group.

The electromagnetic spectrum may be obtained by means of severaltechniques, preferably consisting of a high-resolution electromagneticspectrum, such as an electromagnetic spectrum obtained by:

-   -   a plasma inducing spectroscopy technique, preferably        Laser-induced Breakdown Spectroscopy (LIBS), the spectroscopy        device thereby consisting of a LIBS device and the sensors of        CCDs,    -   Mass Spectroscopy (MS), the spectroscopy device thereby        consisting of a MS device and the sensors of at least one MS        detector,    -   X-Ray Fluorescence (XRF), the spectroscopy device thereby        consisting of an XRF device and the sensor of at least one XRF        detector, or    -   Nuclear Magnetic Resonance Spectroscopy (NMR), the spectroscopy        device thereby consisting of an NMR device and the sensors of at        least one NMR detector.

Moreover, an electromagnetic spectrum may correspond to a physicalsample with a highly complex composition, such as containingconstituents which are unknown. For such a case, prior to step ii), thecalibration method of the present invention may further comprise thesteps of:

-   -   project the obtained electromagnetic spectrum into a feature        space F, the feature space F consisting of a multiple dimension        vector space comprising spectral information on a plurality of        physical samples, the spectral information comprising a one or        more spectral lines having been extracted from a previously        obtained electromagnetic spectrum and) corresponding to a        plurality of known constituents, the spectral information on a        plurality of physical samples being clustered in one or more        groups in the feature space F, according to a predefined        distance between such spectral information,    -   determine a cluster group of the obtained electromagnetic        spectrum and, in such cluster group, determine the spectral        information most similar to the obtained electromagnetic        spectrum,    -   determine the known constituents corresponding to the most        similar spectral information,    -   obtain a theoretical electromagnetic spectrum from such        determined constituents,    -   define the obtained theoretical electromagnetic spectrum as the        theoretical electromagnetic spectrum for steps ii) and iii).

Furthermore, an electromagnetic spectrum may be obtained from aspectroscopy device which contains more than one group of sensors, suchas the case of a LIBS device with two CCDs. In such a case, in which theelectromagnetic spectrum was obtained from a spectroscopy devicecomprising at least two groups of sensors, the calibration method of thepresent invention may further comprise the lengths of such at least twogroups of sensors being merged after the assignment of step iv) andthereby obtaining a full sensor length, preferably the said mergecomprising:

-   -   re-ordering said at least two groups of sensors by their        corresponding wavelength intervals;    -   determining at least one wavelength interval common to the at        least two groups of sensors, as well as the corresponding        spectral lines inside the said at least one common wavelength        interval;    -   determining a relation between the lengths of the at least two        groups of sensors by means of the said corresponding spectral        lines directly, and    -   merging two non-common wavelength intervals and said common        wavelength interval, to obtain the full sensor length.

As previously referred, a highly relevant particular feature of themethod of the present invention is related to the interpretation of thedynamical information structure of emission lines acquired during themolecular breakdown ionization process, whereby each differentconstituent has a spectral fingerprint. Such dynamic information may beidentified and analysed where the referred obtained electromagneticspectrum consists of a plurality of obtained electromagnetic spectra, inparticular a plurality of obtained electromagnetic spectra whichcorrespond to a variation in time, for a certain time-lapse wherein theelectromagnetic spectrum from which at least one spectral line group isobtained in step ii), and thereby from which spectral lines aredeconvoluted in step iii), consists of each of the plurality of obtainedelectromagnetic spectra, each corresponding to a certain time-instantfrom said time-lapse, thereby steps ii) and iii) being performed foreach electromagnetic spectrum corresponding to a certain time-instantfrom said time-lapse, preferably the electromagnetic spectra beingfurther obtained by a plasma inducing spectroscopy technique and thecalibration method further comprising the following steps:

-   -   extracting spectral lines correlated with a specific        time-instant from said time-lapse,    -   from said extracted correlated spectral lines, determining        regions of interest in said time lapse, each region of interest        corresponding to a time interval from said time lapse, the time        interval specifically containing at least one of the extracted        correlated spectral lines and thereby corresponding to the time        of life of specific molecular breakdown ions from the physical        sample,    -   from said regions of interest, defining a temporal sequence of        extracted correlated spectral lines, defining each ion as a node        of a dynamic spectral information network, such network being        established as unique spectral information related to a specific        constituent or plurality of constituents present in the physical        sample, and        more preferably, storing such dynamic spectral information        network and respective constituent or constituents in a        database.

As previously mentioned, it is also an object of the present invention amethod for transferring spectral information obtained from a firstspectroscopy device i and at least a second spectroscopy device j. Suchconsists of another highly relevant feature of the present invention, asit allows to obtain electromagnetic spectra in two different sites, withtwo different spectroscopy devices and physical samples, and stillprovide for the reliable comparison between the electromagnetic spectraobtained in each of such spectroscopy devices. An example is that ofdifferent devices in different places and time, with access to differentsamples, such as several Mining machines, autonomous or remotelyoperated vehicles (ROV) in several locations of a mine, or even indifferent mines, acquiring spectral information on physical samples insuch different locations, the physical samples consisting of rocks insuch locations of the mine(s), and thereby identifying the constituentsof such rocks by means of such spectral information. The method fortransferring spectral information may be supervised or unsupervised. Inthe supervised version, it comprises the steps of:

-   -   assembling a first database with electromagnetic spectra        obtained from the first spectroscopy device i, by means of the        assembly method of the present invention,    -   assembling at least one second database with electromagnetic        spectra obtained from the at least one second spectroscopy        device j, by means of the assembly method of the present        invention, and    -   concatenate a plurality of constituents Y₁ of the sample entries        assembled in the first database with a plurality of constituents        Y of the sample entries assembled in the at least one second        database, thereby obtaining a composition space Y,    -   perform a base transformation in the composition Y and thereby        obtain a feature space K, in which K=UC^(T), U consisting of a        vector of a feature space of constituents and C of a base vector        of K in which U is not maximised, and perform a base        transformation in a composition X consisting of spectral        information corresponding to the concatenated composition Y and        thereby obtain a feature space F, where F=TW^(T), W consisting        of a feature space of spectral information and W of a base        vector of F in which W is not maximised, wherein

K _(i) =T _(i) Q _(i) ^(T) +U _(o,i) Q _(o,i) ^(T)

-   -    in which T_(i) is a covariance feature space, Q_(i) ^(T)        consists of a co-variance basis vector, and U_(o,i) consists of        orthonormal information vector of K_(i), with Q_(o,i) ^(T)        basis, and

F _(i) =T _(i) P _(i) ^(T) +T _(o,i) P _(o,i) ^(T)

-   -    in which P_(i) ^(T) consists of a co-variance basis vector, and        T_(o,i) consists of orthonormal information vector of F_(i),        with P_(o,i) ^(T) basis,    -   determine at least one region of common features in K, thereby        obtaining a one or more common feature regions, preferably by        means of clustering of sample entries in K,    -   determine initial coordinates T_(i→j) ⁰ by direct projection in        the T_(j) feature space, where K_(i)=Q(Q^(T)Q)⁻¹,    -   coordinate warping by warping position T_(i) in T_(j), such that        a co-variance F^(T)K is maximised, until a prediction error is        below a predefined threshold,    -   therefrom reconstruct electromagnetic spectra Xi in Xj, by means        of inverse feature transformation of F→X, thereby providing that        any sample entry obtained by means of the first spectroscopy        device is comparable with any sample entry obtained by means of        the second spectroscopy device.

It is also an object of the present invention an unsupervised method fortransferring spectral information, comprising the steps of:

-   -   perform the method of the previous claim, wherein the        electromagnetic spectra obtained from the at least one second        spectroscopy device and the electromagnetic spectra obtained        from the first spectroscopy device are in respect of physical        samples containing at least one pure element,    -   project an electromagnetic spectrum obtained in the first        spectroscopy device in the feature space F, such electromagnetic        spectrum corresponding to a physical sample containing at least        one unknown constituent,    -   determine a direction of co-variance and a relative position at        co-variance direction,    -   using the initial coordinates T_(i→j) ⁰ find an optimal position        of initial coordinates T_(i→j) by warping along the co-variance        direction, such that rank is ordered and co-variance between        T_(i→j) and T_(j) is maximised between i and j pure element        samples, until the covariance is maximised, thereby providing a        stable rank with [T_(i→j), T_(j)], and    -   therefrom estimate one or more constituents present in the        physical sample containing at least one unknown constituent.

Embodiments

In cooperation with attached drawings, the several embodiments of theobjects of the present invention are herein described.

The several described embodiments are exemplary of specificimplementations of the objects of the present invention, mainly withresort to the example in which the electromagnetic spectrum/spectraconsist of electromagnetic spectrum/spectra obtained by means of aplasma inducing spectroscopy technique such as LIBS.

Reference is made to FIG. 1, provide an overview of an embodiment of thecalibration method of the present invention, as well as of an apparatuswhich implements such method.

Firstly, the method and system disclosed herein comprises continuoussub-optical CCD calibration. To achieve that a time-course plasmaemission high-resolution spectra of a physical sample (S_(i)) isrecorded (S1) and subjected to supervised sub-optical deconvolution (S2)to extract theoretically consistent spectral lines (λ_(i)) andassignment of the spectra lines wavelengths to the CCD length (L) (S3),with correspondent merge of CCD lengths and wavelengths in the case ofmultiple CCD devices (S4) to obtain the continuous calibration functionf(λ,L) (S5).

Secondly, it is also an object of the present invention a method for thetransfer of spectral information between one spectroscopy device and atleast one second spectroscopy device, such method comprising digitalspectral information transfer whereby two or more CCDs (S6) calibratedwith the calibration method of the present invention, thereby havingknown calibration functions, have direct wavelength correspondence (S7)and intensity correspondence using the local representative featurespace method (S8) to piecewise perform all spectral information transferbetween CCDs (S9).

Lastly, it is also an object of the present invention an assemblymethod, which enables to create distributed spectral lines databaseusing independently recorded spectral databases (10) are convertedinside a network of devices, allowing to create a global database (11)that can be shared with other apparatuses.

These objects are further detailed subsequently.

According to a preferred embodiment of the invention, the calibrationmethod of the present invention, which may be referred to—within theterms of LIBS—as continuous sub-optical CCD calibration, is carried outusing electromagnetic spectral information of a physical sample acquiredby plasma emission spectroscopy. The said electromagnetic spectralinformation taken to a physical sample S_(i), is recorded for a givenset of conditions: laser energy and pulse function, wavelengths,atmospheric composition, pressure and temperature; as well as, sampleswith complex composition.

Such embodiment starts with the acquisition of time-course highresolution spectra (S1). A LIBS signal will be used as an example asdepicted in FIG. 2. A typical LIBS signal that encompasses a dynamicemission constituted by a time series (12) from laser ablation, plasmaexpansion with molecular breakdown and ionization, plasma cooling,electron decay, to the ion atomic emission at the latent thermodynamicequilibrium (LTE) (13). For each sample S₁, spectrum intensity isrecorded at different wavelengths (λ) along time (t) (14). When LIBSspectra corresponding to a plurality of physical samples S are recorded(15), these are stored in a 3-way tensor (L) with dimensions S, λ, t(16) and corresponding LTE spectral lines (17).

Results from dynamic emission are therefore processed for sub-opticalspectral lines extraction (λ) (14) using supervised sub-opticaldeconvolution (S2) and analysed for consistency metrics against theexpected theoretical element emission lines (SAHA/LTE emission spectra)stored in a database (15).

Supervised sub-optical deconvolution (S2) is used to accurately extractthe spectral lines (λi) (14). Such is performed by optimizing thedeconvolution against the expected theoretical SAHA/LTE emission spectraof a particular element, so that both are consistent (15). Consistentemission lines databases are stored in the tensor format D(S, λ,t),constituting the dynamical spectral emission lines database (16), for agiven set of laser energy and pulse function, wavelengths; atmosphericcomposition, pressure and temperature. A subset from D(S, λ,t) can beobtained for emission lines at the LTE (17), a static version of theemission lines database.

Reference is made to FIG. 3 to explain the sub-steps of the continuoussub-optical CCD calibration step: i) spectral lines (18) are extractedby sub-optical resolution using supervised deconvolution of pixel basedCCD data (19); ii) a significant number of pure elements and moleculestandards are used to determine their emission lines (20), forcorrespondence with each CCD length (21); and iii) merging the commonCCD regions (22) by their common spectral lines and calculating themerged CCD length, to assign the global calibration function λ^(˜)f(L)(23).

In such embodiment, continuous sub-optical CCD calibration is performedinto three steps. A first step is the initial allocation of wavelengthsusing gas emission spectra—light emission from gas lamps such as Mercury(Mg, ^(˜)250 spectral lines, 200-1204 nm), Argon (Ar, ^(˜)490 spectrallines, 200-1204 nm), Krypton (Kr, 141 spectral lines, 200-1204 nm), Neon(Ne, 591 spectral lines, 200-1204 nm) and Xenon (Xe, 121 lines, 200-1204nm)) are used to perform an initial allocation of spectral lineswavelengths to each CCD length. This initial step allows to betterlocate spectral lines in more complex element emission spectra, such asIron (Fe, ^(˜)6678 Lines 200-1204 nm). As gas lamps presentsignificantly lower number of spectral lines (18), mostly without anyinterference, this allows to obtain very low error in spectral linesextraction and corresponding wavelength allocation to CCD lengths. Theprocess follows by extracting the deconvoluted spectral line (18) by theextracted Point Spread Function (PSF) from the pixel-based dataoptimized against the expected theoretical SAHA/LTE (19) and performingthe CCD length allocation (20) of each extracted spectral line. A firstestimate of the continuous calibration is obtained by joining allspectral lines correspondences to the CCD length (21).

A second step is the wavelength allocation using pure sampleelements—other pure, heavier elements, present significantly a highernumber of emission spectral lines, where many of these, are overlappeddue to spectral resolution and line broadening effects. For example, theion Fe II has spectral lines at 200.31909 nm and 200.39104 nm, whichdistance 71.95 picometers, and therefore will appear super-imposed inthe pixel-based spectra. As heavier elements have a significant numberof spectral lines, they have higher probability of interference betweentwo or more spectral lines. This interference can be estimated bysupervised deconvolution using the theoretical peaks and their relativeintensities, and extracting the spectral lines position by non-negativeoptimization/regression against the theoretically expected SAHA/LTEspectra. These two steps use the interference between spectral lines atdifferent spectrometer resolutions to extract the correct position ofthe spectral lines in the CCD length, significantly reducing thecontinuous CCD wavelength calibration error by extracting spectral linespositions at sub-optical resolutions.

A third step, where the electromagnetic spectrum was obtained from aLIBS device with several CCDs, is the merging multiple CCD wavelengthsinto one continuous CCD—merging the wavelength position of each CCD in amultiple CCD spectrometer system is possible after performing thecontinuous CCD calibration for each CCD. The method thereby determinesthe common wavelength interval and the common spectral lineswavelengths, which form the overlapping CCD pixel region (22). Once theoverlapping correspondence between CCD lengths, given the commonspectral lines, is established, the merged CCD length computed byremoving the overlapping length. The final continuous sub-opticalcalibration (23) is obtained by performing the previous operations tocumulative pairs of CCDs. Merging the overlapping regions allows togreatly reduce the wavelength calibration error in this region, asgratings intensity and interferences provide less resolution andintensities in these overlapping regions.

Therefore, sub-optical continuous wavelength calibration solveshigh-uncertainty in the wavelength and consequently excessive amount ofinterference or in extreme cases, the nonexistence of exclusive spectrallines, presented by the state-of-the art sensor/pixel-based methods thatmethods only use average pixel value to determine theposition/wavelength of the observed (O) spectral lines.

Reference is made to FIG. 4 to present the steps of an embodiment forsub-optical continuous wavelength calibration, which are furtherdetailed in FIGS. 5, 6 and 7. Such sub-optical continuous wavelengthcalibration aims at determining the sub-optical wavelength-CCD lengthcorrespondence by, firstly, optimizing spectral lines matching—where theobserved and SAHA/LTE peaks are binned (S24) given a pre-determinedwavelength (Δλ) and pixels (Δpx) region of interest, determined by theFWHM of a highly coherent laser source (S24), performing intensitycorrection taking into account the number of lines of each group (S25),performing rank matching algorithm (S26) to identify consistent spectrallines between the observed spectra and theoretical spectral estimate;and, secondly, using supervised deconvolution where the matched groups(S27) between the observed and SAHA/LTE spectra are subjected tooptimized deconvolution/convolution and super-imposed process (S28),where the SAHA/LTE is used as the theoretical expectation of theobservation until the convergence to a minimum error (S29), allowing thesub-optical extraction of the observable spectral lines and establishingthe corresponding relationship with the CCD length (S30). The saidsub-optical resolution is so achieved using the theoretical SAHA/LTEspectral lines positions and uncertainties to match the observed spectraby such steps.

Such sub-optical continuous wavelength calibration thereby enables todetermine the calibration function that relates the CCD length (L) tothe wavelength of extracted line positions, solving the convolutionimposed by the limited optical resolution and providing sub-opticalresolution.

FIG. 5 presents in detail the steps of peak binning (S24), relativeintensity correction (S25) and rank matching (S26). The wavelength alongthe CCD length is not possible to be directly estimated from gratingfunctions with reliability, as manufacturing and assembly qualityassurance cannot provide the required reproducibility of this hardware.In this reasoning, wavelengths throughout the CCD length are moreaccurately known by data-driven process of matching the theoreticalwavelength (31) values against the observable lines (32) along the CCDlength.

Peak binning (S24) comprises finding peak groups given a wavelengthinterval for the SAHA/LTE (theoretical) spectrum (31) and pixel intervalfor the observed spectra (32). Therefore, binning the spectral lines ofthe observed (O) and theoretical (P) within a given pixel or wavelengthinterval by distance clustering. The peak binning step (S24) begins byperforming hierarchical clustering based in the Euclidean distancebetween the spectral peaks present in the theoretical SAHA/LTE (P)spectra (31) and the observed (O) spectra. The number of clusters isautomatically optimized so that the number of groups to be rankedbetween O and P is similar through the rank matching step (S26). In theparticular example of Lithium (Li) in FIG. 5, four groups are obtainedin O (32) and P (31), respectively. It would be expected that all groupsG_(P) ¹⁻⁴ (31) and G_(O) ¹⁻⁴ (32) have the same wavelength position andrank intensities but in this practical example relative intensities areinverted between G_(P) ²⁻³ (31) and G_(O) ²⁻³ (32). This interference isdue to the limited resolution of CCD (pixel-based detection technology)that does not allow a determination of peak wavelengths as accurate asdesired. To overcome this interference, a relative intensity correction(S25) step is performed.

Relative intensity correction (S25) is performed by ensuring the correctamount of energy is being compared between G_(P) ²⁻³ and G_(O) ²⁻³groups along the pixels of the corresponding CCD interval. Relativeintensities of spectral lines and groups may not directly match due topixel assignment of convoluted and super-imposed light energy, suchissue being better addressed by correcting the energy per spectral linegiven the number of spectral lines convoluted within a group divide theenergy by pixel or in the case of being in one pixel, as the energy isaccumulated.

Intensity corrections are performed to the binned groups from theobserved spectra (32), taken into consideration the expected intensitiesfrom SAHA/LTE (theoretical) (31). The relative intensity correctionprocess (S25) to obtain an intensity corrected spectrum (33) is asfollows: determine the number of theoretical lines inside a particulargroup; and if lines are centred in one or more pixels, divide thecorresponding energy between the number of theoretical peaks; or if thelines are convoluted along a number of pixels, determine the totalenergy using the following equation E(p)=∫_(p)|(p)dp, and divide by thenumber of expected number of theoretical lines. This step allows toperform more correct grouping of lines, so that supervised deconvolutioncan recover spectral information that is restricted in state-of-the-artdue to the convolution imposed by the limited resolution of CCD.

In this particular example, Li lines with the corresponding wavelengths610.354 nm and 610.365 nm are convoluted into one single pixel.Performing the relative intensity correction, the corrected spectrum(33) is further analysed to adjust the wavelength interval of eachidentified group in the rank matching step (34).

The rank matching step (S26, 34) evaluates the intensity and positionranks for each of the previous groups in k size sequences (3≤k≤n) untilthe full-length size n is reached (the number of binned lines groups),matched between groups of the observed spectra G_(O) and theoreticalspectra G_(P). Non-consistent rank groups, that is, that do not match inposition and intensity are dropped to achieve a very high match (up to100%) in the full spectra between G_(O) and G_(P).

Rank Matching (34) is performed by making [n−k] rank search sequences,sorting groups by their intensities, where n is the number of groups,and k is between 3 and n. The rank search stops once a global rank matchis established (k=n). Therefore, rank matching (34) is the process bywhich the position of the groups and their relative intensities arerelated (G_(P)↔G_(O)), for assignment of a particular observable groupto a given theoretical wavelength interval:

MT=MP+MR

where M_(T) is the global match, M_(P) is the group position match andM_(R) is the group intensity rank match, which must have 100% matchbetween O and P, ensuring full G_(P) and G_(O), correspondence. MP andMR are computed as follows:

M _(P)=[Number of groups in the correct position]/[Total number ofgroups]

M _(R)=[Number of groups with correct intensity rank]/[Total number ofgroups]

To better determine if the correspondence exist, a rank search isperformed sequentially for all ranks, and diagonally for each ranklevel. The method begins to perform a k=3 search, where each k searchmoves forward one group comparison along the CCD length. For example, inthe particular example of Li, two k=3 searches are necessary to computeM_(P)+M_(R). Search that provide 100% match are used to compute the nextrank search level, k=4; that in this particular case k=n. If 100% isobtained in the last level, the correspondence between groups is locked(G_(P)↔G_(O)) and the method can proceed to supervised deconvolution.

FIG. 6 presents peak binning, the relative intensity correction and therank matching steps in samples with non-observed theoretical emissionspectral lines. The emission spectra of mercury (Hg) (35) is used asexample of another inventive aspect of the object of the presentinvention—dealing with unobserved spectral lines due to signal/noiseratio (36), which does not allow to observe all spectral lines in aparticular CCD region (37). Moreover, some elements may not exhibitemission lines in particular regions of the spectrum, such as Hg in theVIS-NIR region (35), not being shown in multiple CCDs devices. Anotherparticularity in multiple CCD devices arises from the grating functionin multiple CCD devices. Gratings are optimized to maximize signalintensity of the mid-range of the CCD pixels, and generally hold lowerintensities at both ends of the CCD, which may be a significantlimitation of these systems with higher errors in wavelength and lowerintensities/sensitivity.

The present embodiment minimizes the loss of information of theoverlapping regions by seeking to maximize all groups' correspondencesin the signal/noise threshold (36). In most cases, theoretical lineswith less intensity may not be observable in these regions. In thisexample, only four out of five groups of spectral lines from theoreticalemission lines are observable. The algorithm identifies by rank matchindexes what theoretical group is not observable, and which spectralline groups can be paired between theoretical and observable, thatprovide 100% match index in CCD length position and ranking.

Furthermore, two other concepts are used: i) correlation filtering; andii) dropping non-observed groups. Correlation filtering determines theSpearman and Pearson correlation coefficient between G_(O) and G_(P),and only highly correlated groups are subjected to intensitycorrections. After intensity correction, groups that are withinG_(P)↔G_(O) correlation, are used in the rank matching process.Non-correlated groups or non-matching sequences are filtered out(dropped). The method proceeds with dropping non-observed spectral linesgroups. This process is illustrated in FIG. 6, beginning with k=3 for Hgin CCD2. In this range, five groups (G_(P) ¹ to G_(P) ⁵) are present intheoretical spectral lines P but the observable spectrum has only fourgroups (G_(O) ¹ to G_(O) ⁴) (38), at least one of the groups must bedropped to determine the G_(P)↔G_(O) relationship.

The embodiment of the method proceeds as follows:

-   -   perform rank k=3 search between G_(P) and G_(O) (38);    -   determine the full match sequences and lock the groups that        exhibit full match;    -   drop work rank group(s);    -   re-calculate ranks at k=3 until M_(P) and M_(R) are 100% (39);        and    -   increase k+1 and repeat steps i-iv until n=k with M_(P) and        M_(R) are 100% (40).

The presented steps of peak binning, relative intensity correction andrank matching ensure that only groups of emission lines that haveconsistency in wavelength position and intensities are used forsupervised deconvolution. In this sense, only validated observablegroups of spectral lines that are convoluted are used for the process ofdeconvolution to extract the exact position of the emission line in theCCD. Consistent groups in position and relative intensities from G_(P)are now a match to G_(O), and therefore the position in wavelengths andrelative intensities can now be used to supervise the deconvolutionprocess.

As previously referred, resolution is lowered by the convolution of theobservable spectra (O) by: optical components (lenses, slit andgrating), natural broadening; thermal effects, Doppler effect,collisional broadening, where:

O(λ)=H*δ(λ_(i))+S

where the observer emission line O(λ) is the convolution of the spectralline Dirac delta δ(λ_(i)) with the effects function H, and super-imposedwith other convoluted spectral lines S. Obtaining the exact location Pof the spectral line δ(λ_(i)), is an objective of superviseddeconvolution, where H is given by:

H(λ,σ,γ)=∫_(−∞) ^(+∞) G(λ,σ)□L(λ,γ)dλ

where:

${G( {\lambda,\sigma} )} = {{\frac{1}{\sigma\sqrt{2\pi}}{\exp( {- \frac{\lambda^{2}}{2\sigma^{2}}} )}\mspace{14mu}{and}\mspace{14mu}{L( {\lambda,\gamma} )}} = \frac{\gamma\text{/}\pi}{\lambda^{2} + \gamma^{2}}}$

where the Voigst profile H can be computed by the different influencesof gratings, slits and Doppler broadening that lead to Gaussian (G)broadening profile, and natural broadening and collisional broadening toLorentzian (L) profile. By manipulating σ and γ, the most importanteffect can be included and corrected by supervised deconvolution.

-   -   Deconvolution is traditionally performed in the Fourier domain:

δ(λ_(i))=F ⁻¹ {F(O)/F(H)}

with smoothing to avoid errors near the signal-noise threshold anddividing by zero. Iterative methods are more immune to noise, and widelyapplied in spectroscopy (e.g. Riley, Van Cittert, Gold,Richardson-Lucy). These need a significant number of iterations toconverge into a physically significant result, which must be verifiedagainst a theoretical result. In most cases, deconvolution is usedempirically without theoretical confirmation, which does not allow todiagnose the statistical and physical validity of this spectroscopypre-processing step.

In this reasoning, supervised deconvolution main objective is tooptimize the convolution function H parameters σ and γ, number ofiterations and exponential boosting, so that, the deconvolution is inaccordance to theoretically expected emission lines, and the position ofthe observed emission lines at the CCD length can be determined withsub-optical accuracy.

FIG. 7 presents the concepts of supervised deconvolution step, in anembodiment of the calibration method of the present invention, where theposition of the theoretical spectral lines are estimated bydeconvolution of the original signal (O) and convolution the SAHA/LTElines (P) for a particular group of lines (G_(P) ↔G_(O)) by non-negativeoptimization of j=argmin(|O_(dec)−CP_(conv) ^(t)|), where C isnon-negative super-imposed mixture matrix, and O is deconvoluted(thereby obtaining O_(dec), as previously referred) and P is convoluted(thereby obtaining P_(conv), also as previously referred) accordinglyusing the Voigst profile as the point-spread-function (PSF). FIG. 7illustrates how to match two observed groups (G_(O)) (7.36) andtheoretical SAHA/LTE groups (G_(P)) (7.37). The observable (O) spectra(7.36) can be regarded as the convolution of optical components andsuper-position of the SAHA/LTE plus uncertainties. Therefore, the exactwavelength position of the spectral lines δ(λ_(i)) is reliably estimatedby a supervised deconvolution process that matches the convolution andsuperposition of theoretical spectra by the following non-negativeoptimization:

j=argmin([O _(dec) −CP _(conv) ^(t)]²)

where O_(dec), C and P are always non-negative, and C is thesuper-position vector. In order to ensure non-negativity, C vectorsolution space is confined to a convex hull cone (7.35), to which theboundaries are confined by the expected theoretical intensityrelationships between spectral lines within a particular group(G_(P)↔G_(O)). Supervised deconvolution ensures that intensities andlines positions are correctly balanced (7.39, 7.40, 7.41), so that,their position along the CCD length is determined with significantsub-optical accuracy (7.42).

Supervised deconvolution provides the deconvolution of the observablespectra to optimize the position and intensities of spectral lines,between O and P, where both similarity between O_(dec) and P(E₀=Σ[O_(dec)−CP_(conv)]²/n_(i)) and position invariance(E_(P)=Σ[P^(i)−P^(i+1)]²/n_(i)) are optimal criteria. The algorithmbegins to spawn the initial combinations of H(λ, σ,γ), number ofconvolutions, and boosting factor, and initial super-position vector.Within each combination, optimization is performed by the followingsteps:

-   -   Deconvolution of the observable spectra: performing        deconvolution with n boosted iteration until a new O^(i+1) is        obtained;    -   Theoretical spectra generation: generate the theoretical        SAHA/LTE using spread function and super-position vector    -   Sub-optical spectral lines position determination: non-negative        optimization of j=argmin([O_(dec)−CP_(conv) ^(t)]²), where        optimized C is non-negative and spectral lines position P is        warped to determine the best optimal position of each line at        the CCD length.

The algorithm repeats for a new non-negative search, adding a newcombination search until the threshold criteria for E₀ and E_(P) areobtained. The algorithm resolves the position P of the spectral linesgroups in the observable spectra, being possible to the assignment ofspectral lines theoretical wavelengths to the CCD length. Sub-opticalspectral lines (29) are obtained the peak of the optimized correspondingPSF.

Another inventive aspect of the invention is the process of sub-opticalspectral lines extraction for unknown complex samples. When complexsamples with unknown composition are subjected to peak binning, groupingand supervised deconvolution, they need to be supervised by samples thatprovide high similarity of features, that is, spectral groups andtheoretical spectral lines from a significant number of elements must beused to accurately extract the position of the expected emission lines.Therefore, the steps of spectral lines grouping and superviseddeconvolution can be accurately used once a similar sample in thefeature space is used to supervise the deconvolution. Two differentapproaches are used: SAHA/LTE feature space simulation—the SAHA/LTEequations are used to create a theoretical spectra (P) feature-spacecorresponding to a plurality of complex compositions of constituents tosupervise the deconvolution; and data driven feature space—wherecompositional information about the sample (γ) and corresponding spectra(O) are experimentally obtained to create the feature space (F) andcorrespondent expected theoretical spectra (P). For any of theseoptions, once unknown samples are recorded, binning, matching andsupervised deconvolution proceeds as follows:

-   -   project the unknown sample spectra into the feature-space F;    -   determine the most similar sample from a cluster group;    -   determine the corresponding constituents;    -   compute or obtain the theoretical spectra (P) from SAHA/LTE        equations; and    -   use the O and P as input into step ii) of the calibration method        of the present invention for binning, matching and supervised        deconvolution.

FIG. 8 presents the steps for dynamic database construction, in anembodiment of the calibration method of the present invention, where thedynamic plasma-emission breakdown process is recorded from ablation tothe LTE (43). Dynamical information is extracted for each time step (44)and LTE (45), extracting sub-optical spectral lines by the samepreviously described steps of binning, matching and superviseddeconvolution. Time-course correlated lines (46) are extracted (47),leading to the existence of consistent time-course regions of interest(ROI) (48) that correspond to the time of life of specific molecularbreakdown ions. Once all molecular bonds are broken, the LTE spectra isobservable (45). Such involves the extraction of each line (46) andcorrespond life time (47) and the definition of region of interest (ROI)(48). ROI's that have synchronized life times, belong to the sameconstituent, and therefore, a map of ROI's can be constructed (49) thattakes into consideration the breakdown process (44) and LTE (45) foreach sample, where ion emission lines are extracted by ROI'ssynchronization (50).

Extracted ROI's compose the ROI sample map (49), a specific dynamicalsample fingerprint of the breakdown process, from where informationabout specific breakdown ions is extracted (51): specific lines and timeof life, sequential breakdown network (52) and corresponding kinetics(53) until the LTE. Automatically extracted dynamic and LTE spectralsub-optical lines and information is stored in a high dimensionaltensor. The ROI map provides information to determine theplasma-breakdown network (PBN) (51). PBN is generated from the temporalsequence of ion emission lines, to which each ion correspond to a nodeof the network. Each ion is formed by a specific plasma breakdownreaction (53). The kinetic information and time of life of each ion,provides information about the molecular structures present in eachsample, as well as, composition, until the LTE is reached and onlyemission lines from atomic ions are observable (54).

The extracted information is organized into the multi-dimensional tensorformat (samples, time, wavelengths) (43). Each sample is represented bythe extracted sub-optical lines throughout time until the LTE. Tensordatabase (samples, time, wavelength), where each sample hascorresponding associated information about the breakdown network.Furthermore, the final step determines each constituent the informationabout spectral lines global and local exclusivity, interference anduniqueness. Furthermore, each recognized ion constituent has associatedthe following information ions and extracted lines with correspondingtime of life, kinetics and breakdown network for each sample. Moreover,extracted lines are classified as:

-   -   exclusive: spectral lines that at a given spectral resolution        are exclusive of a particular ion, and therefore provide a        deterministic identification. Spectral lines can also have        context exclusivity, that is, for are exclusive for a given        sub-group or class;    -   interference: spectral lines that are unresolved between        constituents, holding information on the interference patterns        that can be used for quantification and classification;    -   unique: spectral lines that are exclusive in the context of the        breakdown process, and provide a direct identification of the        molecular structure. As exclusivity, uniqueness can also be        restricted to a particular class or sub-group.

Another object of the invention relates to the capacity to transferinformation between different spectrometer systems without the use ofstandardization, removing the current disadvantages of the need for amaster spectroscopy system, sample standards or re-calibration.Information transfer between different observations are dominated byoptical effects of components such as slit, grating and CCD, laserenergy and pulse function; and samples diversity. Optical componentsgenerate distortions to the same spectral information, so that, theobserved signal is unique to a particular device, despite spectralinformation is the same. Therefore, information transfer can be regardedas a correction between feature-space distortions of different devices.

Continuous CCD calibration enables the direct transfer of wavelengthpositions in the CCD between different spectrometers (12), as follows:

λ_(CCD1) =f(L ₁,λ)

λ_(CCD2) =f(L ₂,λ)

from where, the direct relationship is established: λ→(L₁,L₂) (13).As depicted in FIG. 9, spectral information transfer may be performed bysupervised (55) and unsupervised (59) steps. The main advantages of thisnew technology are:

Independently recorded spectra and constituent composition can beregarded as an individual database [X₁/L₁,Y₁], [X₂/L₂,Y₂], [X₃/L₃,Y₃] .. . [X_(n)/L_(n),Y_(n)], where X is observed spectral data, L dynamictensors, and Y the constituents/sample composition assumed as groundtruth. Data of each database is not reliably transferable betweenapparatuses, and must be corrected between each X₁, X₂, X₃ . . . X_(n)or L₁, L₂, L₃ . . . L_(n).

Standardization uses the same samples across different devices,enforcing the same information in Y, X or L. No matter the differencesin the observable signal X_(i) or L_(i), the information aboutconstituents is equivalent. The same is valid for similar Y's, Y₁ ^(˜)Y₂^(˜)Y₃ . . . Y_(n), and X₁ ^(˜)X₂ ^(˜)X₃ . . . ^(˜)X_(n), which alsoprovide equivalent information about concentration co-variance, despiteoptical artefacts that make each one of the observations unique.

Spectral distortions can be regarded as rotational warping of thefeature space, as presented in FIG. 9. Both X or L and Y can besubjected to a base transformation (e.g. kernel, Fourier, wavelets,curvelets, eigenvectors or other basis, that can be made orthonormal),to provide the corresponding feature-spaces F and K, respectively. F andK are chosen so that constituents' quantification features (U) has acorresponding information in the spectral feature-space (T), T→U:

j(w,c)=argmax(t ^(t) u)

where F=TW^(t) and K=UC^(t). which if they carry the same informationT=U, meaning that F and K hold the same information geometry oreigenstructure.

In this reasoning, any device feature-space T resulting from theobserved features F, must hold the same information about K, despite thedifferent devices have unique observations (O). Information betweenspectral features are transferable between the different T₁, T₂, T₃ . .. T_(n) spaces, supervised by Y.

FIG. 9 presents additional features for supervised spectral informationtransfer (55). In this particular example, three independentspectroscopy devices have their own unique databases, with correspondingconstituents composition Y and the observed spectral data X (56):[X₁,Y₁], [X₂Y₂], [X₃,Y₃]. Each independent device is capable ofestablishing the following relationships within a local geometry of thefeature space:

F _(i) =T _(i) P ^(t) _(i) +T _(o,i) P ^(t) _(o,i)

K _(i) =T _(i) Q ^(t) _(i) +U _(o,i) Q ^(t) _(o,i)

where T_(i) is the co-variance feature space, P^(t) _(i) and Q^(t) _(i)the corresponding F_(i) and K_(i) co-variance basis, T_(o,i) and U_(o,i)the orthonormal information to F_(i) and K_(i), with P^(t) _(o,i) andQ^(t) _(o,i) basis. Only T holds common information between Y and X/L,being the transferable information. FIG. 10. is presented as auxiliaryto provide the necessary steps of supervised spectral informationtransfer. Information transfer can be regarded as a correction betweenfeature spaces T of different devices, within shared information orlocal regions/geometries of the said feature-space T (57).

Supervised information transfer between two independent devices i and jthat share a region of the feature-space, is performed by the followingsteps that convert the information of i in j:

-   -   assembling the constituents composition space (Y) (S63):        concatenate the constituents databases Y_(i) and Y_(j) into Y        (Y←[Y_(i),Y_(j)]);    -   determine the common feature region (S64): information transfer        can only be performed between regions with common information        of K. In this sense, the clustering of samples in K space is        performed to supervise the samples that belong to a common        region (57);    -   determine the initial T⁰ _(i→j) (S65) coordinates by direct        projection in the T_(j) feature space, T⁰ _(i→j)=Y_(i)        Q(Q^(t)Q)⁻¹;    -   coordinate warping (S66): warp T_(i) position in T_(j), so that        is maximizes the co-variance F^(t)K, j=argmax(t^(t)u) (S67). For        each iteration of T_(i+1) in T_(j) space, the estimated spectra        X_(i+1) is computed at device J, by taking into account the        orthogonal information to K, T_(o,i) that must also be        determined, so that:

F _(i+1) =T _(i+1) P ^(t) _(i) +T _(o,i+1) P ^(t) _(o,i)

Kp _(i+1) =T _(i+1) Q ^(t) _(i) +U _(o,+1i) Q ^(t) _(o,i)

where F_(i+1) is the estimated spectral feature that predicts Kp_(i+1)

-   -   the warping step is performed until the prediction error is        below a given threshold (S68): e_(i)=Σ[Kp−K]²;    -   reconstruction of the initial Spectra X_(i) in X_(j), by inverse        feature transformation F→X (especial case when F=X, no        transformation is necessary).

Given the previous steps, spectral information is reliably transferablebetween two apparatuses/devices i and j, and both constituentcomposition and estimated spectra be added to the j device, where[Y_(i),X_(i)] is transferable to the database [Y_(j+1),X_(j+1)]. Thesame can be extended to any pair of devices in the network that share aregion of the K feature space. Therefore, for any given new spectra isonly know to device i, can now be used by device j, to predict theconstituents composition.

These steps can also be performed by a chain rule (58) to sequentiallycovering the feature space by different devices where information can besequentially transferred along the network to devices that never hadaccess to similar samples. If information from i fully transferred to j,and k has no knowledge of i, but has of j, i→j transferred informationis available to k.

Relevant aspects of unsupervised information transfer are presented inFIG. 9 (59). Unsupervised uses only observed spectral information X andits structure to transfer information between different devices. Devicesonly register in databases their independent spectra [X₁], [X₂], [X₃],and therefore spectral information cannot be reliably transferred withconstituent composition supervision. This aspect of the invention ishighly relevant for exploration, when there is no availability tosupervise the system using reference ground truth methods for sampleconstituents identification and quantification.

Unsupervised spectral transfer is supported by two main characteristics:i) the feature-space of pure elements is known, as these were previouslyused to performing sub-optical calibration; and ii) spectral informationtransfer is performed by analysing the coordinates in the feature spaceand co-variance direction of constituents quantification, that must bepreserved when transferred between i and j apparatuses/devices (S60).

FIG. 10. provides auxiliary information about the main steps ofunsupervised spectral information transfer:

-   -   pure elements feature-space information transfer (S71):        transferring information of pure elements between devices i and        j is performed by the same steps of supervised information        transfer (S63-S70), where constituents' composition Y is given        by pure elements incidence matrix,    -   projecting unknown samples into pure elements spaces (S72):        project the spectra of device i into the pure elements space of        j, T_(j), given by the exclusive spectral lines of observed        elements in i and j,    -   co-variance direction determination (S73) and relative position        at co-variance direction (S74): to fine tune the sampling of        X_(i) and X_(j) that can be used in the T_(i→j) projection,    -   warping T_(i→j) projection (S75): using the initial relative        position, find the optimal position of T_(i→j) by warping along        the co-variance direction, so that, rank is correctly ordered,        and covariance between T_(i→j) and T_(j) direction is maximized        between i and j samples. Perform the warping of T_(i→j) until        the covariance is maximized, that is, a stable rank is predicted        with [T_(i→j), T_(j)] (S76),    -   estimate the observable spectra by X_(i→j)=T_(i→j) P^(t) (S77).

From this steps, any new unknown spectra that is projected into thisregion the spectral feature space, can be directly and reliably comparedto other samples, to rank the content in constituents and providesimilarity metrics with known samples (S78).

As will be clear to one skilled in the art, the present invention shouldnot be limited to the embodiments described herein, and a number ofchanges are possible which remain within the scope of the presentinvention.

Of course, the preferred embodiments shown above are combinable, in thedifferent possible forms, being herein avoided the repetition all suchcombinations.

REFERENCES

-   Kramida, A., Ralchenko, Yu., Reader, J. and NIST ASD Team (2018).    NIST Atomic Spectra Database (version 5.5.6), [Online]. Available:    https://physics.nist.gov/asd [Tue May 292018]. National Institute of    Standards and Technology, Gaithersburg, Md.-   D. W. Hahn and Omenetto N. Laser-induced breakdown spectroscopy    (libs), part i: review of basic diagnostics and plasma-particle    interactions: still-challenging issues within the analytical plasma    community. Appl Spectrosc., 64(12):335-66, 2010.-   A. Cousin, O. Forni, S. Maurice, O. Gasnault, C. Fabre, V.    Sautterd, R. C. Wiense, and J. Mazoyera. Feasibility of generating a    useful laser-induced breakdown spectroscopy plasma on rocks at high    pressure: preliminary study for a Venus mission. Spectrochim. Acta    Part B, 59:987-999, 2011.

1. A calibration method of a spectroscopy device comprising a pluralityof sensors characterised in that the calibration method comprises thesteps of: i) obtaining a high-resolution electromagnetic spectrum of aphysical sample, the electromagnetic spectrum being obtained by means ofthe plurality of sensors of the spectroscopy device, ii) obtaining, bydetermining peak groups of the electromagnetic spectrum within awavelength interval from a plurality of predefined wavelength intervals,and matching each peak group with at least one theoretical spectral linewithin such interval, at least one spectral line group (O) from theelectromagnetic spectrum, a spectral line group containing at least onespectral line, iii) optimising a deconvolution of each obtained spectralline group against at least one theoretical electromagnetic spectrum,and thereby extracting at least one spectral line from each spectralline group, in particular obtaining a wavelength associated to eachextracted spectral line, the optimisation being performed untilconvergence of each spectral line group (O) with an at least onespectral line of a theoretical spectrum (P), with a predefined minimumerror, iv) assigning each obtained wavelength to one or more sensors ofthe plurality of sensors of the spectroscopy device, and therebycorresponding each wavelength to a wavelength position in the wholesensor length, the sensor length being defined by the plurality ofsensors, and v) from the correspondence of each wavelength to awavelength position in the sensor length, determining a calibrationfunction of the spectroscopy device.
 2. A calibration method accordingto claim 1 wherein obtaining at least one spectral line under step ii)comprises: a) binning each peak group of the electromagnetic spectrum, apeak group containing at least one peak, by determining peak groups ofthe electromagnetic spectrum within a wavelength interval from aplurality of predefined wavelength intervals and by comparing peakgroups within such predefined wavelength intervals with spectral linesof a theoretical spectrum, wherein the number of peak groups of theelectromagnetic spectrum and of the theoretical spectrum are the same,b) correcting the relative intensity of each binned peak group anddefining a rank of each peak group according to a correspondingcorrected intensity, said correction being performed by comparison withthe intensities of corresponding theoretical spectral lines, and c)matching the rank of each peak group, by iteratively assigning awavelength position of a peak group and corresponding a correctedintensity with at least one theoretical spectral line within suchinterval, and thereby obtaining one or more spectral line groups.
 3. Acalibration method according to claim 2 wherein the peak binning of stepa) specifically comprises performing wavelength distance clusteringbetween the obtained peak groups and corresponding theoretical spectrallines, thereby determining peak groups of the electromagnetic spectrumwithin a wavelength interval.
 4. A calibration method according to claim2 wherein the comparison with the intensities of correspondingtheoretical spectral lines of step b) specifically comprises: determinethe number of spectral lines of the theoretical electromagnetic spectruminside a peak group of the obtained electromagnetic spectrum, and ifsuch peak group is centred in one or more sensors of the spectroscopydevice, divide the energy of such peak group between the number ofspectral lines of the theoretical electromagnetic spectrum, or,alternatively, if the peak group is convoluted along more than onesensor, determine the total energy of the peak group in such sensors anddivide such total energy by the number of convoluted theoreticalspectral lines, thereby correcting the relative intensity of obtainedpeak groups based on the corresponding theoretical spectral lines and/orwherein the rank matching of step c) specifically comprises: performingSS=n−k rank search sequences (SS), n consisting of the number of peakgroups and k being an integer between 3 and n, with matching betweenpeak groups of the obtained electromagnetic spectrum and spectral linesof the theoretical electromagnetic spectrum, sorting peak groups bytheir intensities until a global rank match is established, the globalrank match being established when a full length is reached, therebyobtaining one or more spectral line groups and where a peak group doesnot match in wavelength position and intensity with a spectral line ofthe theoretical spectrum, discarding such peak group, thereby providinga complete match between peak groups of the obtained electromagneticspectrum and spectral lines of the theoretical electromagnetic spectrum.5. A calibration method according to claim 1 wherein the deconvolutionof step iii) comprises optimising the wavelength position andintensities of spectral lines within a spectral line group between eachspectral line group and a theoretical electromagnetic spectrum,specifically by means of the optimisation of similarity and wavelengthposition invariance between each spectral line group and a theoreticalelectromagnetic spectrum and, such optimisation comprising theestimation of the wavelength position of theoretical spectral lines bydeconvolution of the obtained electromagnetic spectrum (O) andconvolution of the referred theoretical spectral lines (P) within aspectral line group, by non-negative optimization of:j=argmin(|0−CP ^(T)|), where C is a non-negative matrix which defines aconvolution and superposition of spectral lines, P^(T) consisting of thetransposed of P, and O is deconvoluted—thereby obtaining a deconvolutedO (O_(dec))—and P is convoluted—thereby obtaining a convoluted P(P_(conv))—so optimized aiming a match between O and P, therebyextracting at least one spectral line.
 6. A calibration method accordingto claim 1 wherein said electromagnetic spectrum consists anelectromagnetic spectrum obtained by: a plasma inducing spectroscopytechnique, preferably Laser-induced Breakdown Spectroscopy (LIBS), thespectroscopy device thereby consisting of a LIBS device and the sensorsof CCDs, Mass Spectroscopy (MS), the spectroscopy device therebyconsisting of a MS device and the sensors of at least one MS detector,X-Ray Fluorescence (XRF), the spectroscopy device thereby consisting ofan XRF device and the sensor of at least one XRF detector, or NuclearMagnetic Resonance Spectroscopy (NMR), the spectroscopy device therebyconsisting of an NMR device and the sensors of at least one NMRdetector.
 7. A calibration method according to claim 1 wherein prior tostep ii), it further comprises the steps of: project the obtainedelectromagnetic spectrum into a feature space F, the feature space Fconsisting of a multiple dimension vector space comprising spectralinformation on a plurality of physical samples, the spectral informationcomprising a one or more spectral lines having been extracted from apreviously obtained electromagnetic spectrum and) corresponding to aplurality of known constituents, the spectral information on a pluralityof physical samples being clustered in one or more groups in the featurespace F, according to a predefined distance between such spectralinformation, determine a cluster group of the obtained electromagneticspectrum and, in such cluster group, determine the spectral informationmost similar to the obtained electromagnetic spectrum, determine theknown constituents corresponding to the most similar spectralinformation, obtain a theoretical electromagnetic spectrum from suchdetermined constituents, define the obtained theoretical electromagneticspectrum as the theoretical electromagnetic spectrum for steps ii) andiii).
 8. A calibration method according to claim 1 wherein theelectromagnetic spectrum was obtained from a spectroscopy devicecomprising at least two groups of sensors, the lengths of such at leasttwo groups of sensors being merged after the assignment of step iv) andthereby obtaining a full sensor length, preferably the said mergecomprising: re-ordering said at least two groups of sensors by theircorresponding wavelength intervals; determining at least one wavelengthinterval common to the at least two groups of sensors, as well as thecorresponding spectral lines inside the said at least one commonwavelength interval, and determining at least one wavelength intervalwhich is not common to the at least two groups of sensors; determining arelation between the lengths of the at least two groups of sensors bymeans of the said corresponding spectral lines directly, and merging twonon-common wavelength intervals and said common wavelength interval, toobtain the full sensor length.
 9. A calibration method according toclaim 1 wherein the referred obtained electromagnetic spectrum consistsof a plurality of obtained electromagnetic spectra, in particular aplurality of obtained electromagnetic spectra which correspond to avariation in time, for a certain time-lapse wherein the electromagneticspectrum from which at least one spectral line group is obtained in stepii), and thereby from which spectral lines are deconvoluted in stepiii), consists of each of the plurality of obtained electromagneticspectra, each corresponding to a certain time-instant from saidtime-lapse, thereby steps ii) and iii) being performed for eachelectromagnetic spectrum corresponding to a certain time-instant fromsaid time-lapse, preferably the electromagnetic spectra being furtherobtained by a plasma inducing spectroscopy technique and the calibrationmethod further comprising the following steps: extracting spectral linescorrelated with a specific time-instant from said time-lapse, from saidextracted correlated spectral lines, determining regions of interest insaid time lapse, each region of interest corresponding to a timeinterval from said time lapse, the time interval specifically containingat least one of the extracted correlated spectral lines and therebycorresponding to the time of life of specific molecular breakdown ionsfrom the physical sample, from said regions of interest, defining atemporal sequence of extracted correlated spectral lines, defining eachof said ions as a node of a dynamic spectral information network, suchnetwork being established as unique spectral information related to aspecific constituent or plurality of constituents present in thephysical sample, and more preferably, storing such dynamic spectralinformation network and respective constituent or constituents in adatabase.
 10. A method for assembling at least one electromagneticspectrum database wherein it comprises the steps of: calibrating aspectroscopy device through a calibration method of a spectroscopydevice comprising a plurality of sensors, wherein such calibrationmethod in turn comprises the steps of: i) obtaining a high-resolutionelectromagnetic spectrum of a physical sample, the electromagneticspectrum being obtained by means of the plurality of sensors of thespectroscopy device, ii) obtaining, by determining peak groups of theelectromagnetic spectrum within a wavelength interval from a pluralityof predefined wavelength intervals, and matching each peak group with atleast one theoretical spectral line within such interval, at least onespectral line group (O) from the electromagnetic spectrum, a spectralline group containing at least one spectral line, iii) optimising adeconvolution of each obtained spectral line group against at least onetheoretical electromagnetic spectrum, and thereby extracting at leastone spectral line from each spectral line group, in particular obtaininga wavelength associated to each extracted spectral line, theoptimisation being performed until convergence of each spectral linegroup (O) with an at least one spectral line of a theoretical spectrum(P), with a predefined minimum error, iv) assigning each obtainedwavelength to one or more sensors of the plurality of sensors of thespectroscopy device, and thereby corresponding each wavelength to awavelength position in the whole sensor length, the sensor length beingdefined by the plurality of sensors, and v) from the correspondence ofeach wavelength to a wavelength position in the sensor length,determining a calibration function of the spectroscopy device, andwherein the said method for assembling at least one electromagneticspectrum database wherein further comprises the steps of: obtaining anelectromagnetic spectrum of a physical sample by means of the pluralityof sensors of the calibrated spectroscopy device, obtaining at least onespectral line from the electromagnetic spectrum of the calibratedspectroscopy device and determining one or more constituents present insuch physical sample, and storing such at least one spectral lineobtained from the electromagnetic spectrum X and correspondingdetermined one or more constituents Y in said at least oneelectromagnetic spectrum database, thereby assembling an entrycorresponding to a sample.
 11. A method for transferring spectralinformation obtained from a first spectroscopy device i and at least asecond spectroscopy device j, wherein it comprises the steps of:assembling a first database with electromagnetic spectra obtained fromthe first spectroscopy device i, by means of the method of claim 10,assembling at least one second database with electromagnetic spectraobtained from the at least one second spectroscopy device j, by means ofthe method of claim 10, and concatenate a plurality of constituentsY_(i) of the sample entries assembled in the first database with aplurality of constituents Y_(j) of the sample entries assembled in theat least one second database, thereby obtaining a composition space Y,perform a base transformation in the composition Y and thereby obtain afeature space K, in which K=UC^(T), U consisting of a vector of afeature space of constituents and C of a base vector of K in which U isnot maximised, and perform a base transformation in a composition Xconsisting of spectral information corresponding to the concatenatedcomposition Y and thereby obtain a feature space F, where F=TW^(T), Wconsisting of a feature space of spectral information and W of a basevector of F in which W is not maximised, whereinK _(i) =T _(i) Q _(i) ^(T) +U _(o,i) Q _(o,i) ^(T) in which T_(i) is acovariance feature space, Q^(T) _(i) consists of a co-variance basisvector, and U_(o,i) consists of orthonormal information vector of K_(i),with Q_(o,i) ^(T) basis, andF _(i) =T _(i) P _(i) ^(T) +T _(o,i) P _(o,i) ^(T) in which P^(T) _(i)consists of a co-variance basis vector, and T_(o,i) consists oforthonormal information vector of F_(i), with P_(o,i) ^(T) basis,determine at least one region of common features in K, thereby obtaininga one or more common feature regions, preferably by means of clusteringof sample entries in K, determine initial coordinates T_(i→j) ⁰ bydirect projection in the T_(j) feature space, where K_(i)=Q(Q^(T)Q)⁻¹,coordinate warping by warping position T_(i) in T_(j), such that aco-variance F^(T)K is maximised, until a prediction error is below apredefined threshold, therefrom reconstruct electromagnetic spectra Xiin Xj, by means of inverse feature transformation of F→X, therebyproviding that any sample entry obtained by means of the firstspectroscopy device is comparable with any sample entry obtained bymeans of the second spectroscopy device.
 12. A method for transferringspectral information obtained from a first spectroscopy device i and atleast a second spectroscopy device j, wherein it comprises the steps of:perform the method of the previous claim, wherein the electromagneticspectra obtained from the at least one second spectroscopy device andthe electromagnetic spectra obtained from the first spectroscopy deviceare in respect of physical samples containing at least one pure element,project an electromagnetic spectrum obtained in the first spectroscopydevice in the feature space F, such electromagnetic spectrumcorresponding to a physical sample containing at least one unknownconstituent, determine a direction of co-variance and a relativeposition at co-variance direction, using the initial coordinates T_(i→)⁰, find an optimal position of initial coordinates T_(i→j) by warpingalong the co-variance direction, such that rank is ordered andco-variance between T_(i→j) and T_(j) is maximised between i and j pureelement samples, until the covariance is maximised, thereby providing astable rank with [T_(i→j), T_(j)], and therefrom estimate one or moreconstituents present in the physical sample containing at least oneunknown constituent.
 13. A computational apparatus for the calibrationof a spectroscopy device comprising a plurality of sensors, wherein itis configured to implement the method of claim 1 or of claim 10 or ofclaim 11, optionally further comprising a spectroscopy device which: isable to induce a plasma state in a physical sample, said spectralinformation being obtained from said spectroscopy device, thespectroscopy device preferably consisting of a plasma inducingspectroscopy technique, more preferably a LIBS device, consists of an MSdevice, the sensors thereby consisting of at least one MS detector,consists of an XRF device, the sensors thereby consisting of at leastone XRF detector, or consists of an NMR device, the sensors therebyconsisting of at least one NMR detector.
 14. A network of computationalapparatuses, each computational apparatus comprising a database andbeing configured to implement the method of claim 11, thereby assemblingsuch database, each computational apparatus being further configured toimplement the method of claim 12 wherein, for each computationalapparatus, any sample entry obtained by means of a first spectroscopydevice is comparable with any sample entry obtained by means of thesecond spectroscopy device.
 15. Non-transitory storage media includingprogram instructions executable to carry out the method of: claim 1,claim 10, and/or claim 11.